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Record W6922044813 · doi:10.11575/prism/39741

Children's Music Education in a Second Language: A Qualitative Case Study

2022· other· en· W6922044813 on OpenAlexaboutno aff

Bibliographic record

VenueOpen MIND · 2022
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumPianoMusic educationFocus groupQualitative researchTeaching methodPeriod (music)Language acquisition

Abstract

fetched live from OpenAlex

This study examined how the teaching and learning processes of music and piano concepts occur in a bilingual environment when students have yet to master English as an additional language. The focus of this inquiry was on the students’ learning outcomes related to music as well as the methodologies and teaching practices used by the teacher to facilitate the students’ learning within the classroom. In order to understand this phenomenon, the study was conducted using the methodology of a single case study. The case was a group of immigrant children who lived in Canada for less than two years and spoke other languages than English at home. They had limited proficiency in the English language and their ages varied from 5 to 9 years old. The teacher, who was a volunteer in this study, was born in Canada and only spoke English. The study consisted of eight weekly 30-minute lessons led by the teacher. Data were collected through three different methods: Lesson observations, interviews with participants, and analysis of curriculum documents. The results indicate that three elements were essential for the learning process to happen: (1) The teacher’s methodology, pedagogy, and approach; (2) the students’ ways of learning—their creativity, curiosity, motivation, and behaviour; and (3) the role of a well-designed and structured curriculum that contained essential music content and introductory piano skills aimed to be taught to children ages 5 to 9 years old.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0120.006
Scholarly communication0.0040.002
Open science0.0020.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.042
GPT teacher head0.420
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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